Due to the page limit, we briefly review the existing work on graph neural networks and fairness-aware learning on graphs. A more comprehensive discussion can be found in Appendix . Existing GNNs can be roughly divided into spectral-based and spatial-based GNNs. Spectral-based GNNs provide graph convolution definition based on graph theory [1]}, [2]}, [3]}. Spatial-based GNNs variant are popular due to explicit neighbors' information aggregation, including Graph con-volutional networks (GCN) [4]}, graph attention network (GAT) [5]}. The unified optimization framework are also provided to unify many existing message passing schemes [6]}. As for fairness in graph data, many works have been developed to achieve fairness in machine learning community [7]}, including fair walk [8]}, adversarial debiasin [9]}, Bayesian approach [10]}, and contrastive learning [11]}. However, aforementioned works ignore the role of message passing in terms of fairness. In this work, we theoretically and experimentally reveal that GNNs aggregation schemes enhance node representation bias under topology bias, and develop a efficient and transparent fair message passing scheme via utilizing useful information of neighbors and debiasing node representation simultaneously.
